Randomly Projected KD-Trees with Distance Metric Learning for Image Retrieval

Randomly Projected KD-Trees with Distance Metric Learning for Image Retrieval

Efficient nearest neighbor (NN) search techniques for highdimensional data are crucial to content-based image retrieval (CBIR). Traditional data structures (e.g., kd-tree) usually are only efficient for low dimensional data, but often perform no better than a simple exhaustive linear search when the...

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Bibliographic Details
Main Authors: WU, Pengcheng, HOI, Steven, NGUYEN, Duc Dung, HE, Ying
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2011
Subjects:
Computer Sciences
Databases and Information Systems
Online Access:https://ink.library.smu.edu.sg/sis_research/2356
https://ink.library.smu.edu.sg/context/sis_research/article/3356/viewcontent/Randomly_Projected_KD_Trees_with_Distance_Metric_Learning_for_Image_Retrieval.pdf
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Institution: Singapore Management University
Language: English

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https://ink.library.smu.edu.sg/sis_research/2356
https://ink.library.smu.edu.sg/context/sis_research/article/3356/viewcontent/Randomly_Projected_KD_Trees_with_Distance_Metric_Learning_for_Image_Retrieval.pdf

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